Biomarkers are not validated or standardised

A Japanese trial found vitamin D supplements did not help everyone after digestive cancer surgery, but appeared to help a subgroup identified by a tumour marker. That subgroup deserves its own trial.

Before cancer spreads, distant organs are changed to become welcoming. A test for those changes would show which organ is at risk while a person still looks cancer-free.

Volatile compounds in breath differ in cancer. A breath test validated in truly symptomatic patients, not lab volunteers, could tell GPs who needs urgent scans and who can safely wait.

Blood and tissue samples collected in cancer trials are the best material for validating new tests, but most sit unused under contracts that make access impossible. A commons would make them available for approved research.

Completed trials have stored samples. Testing a new biomarker on them with the plan written in advance is nearly as good as a new trial and far cheaper, but regulators have no clear route to accept it.

Tests for 'HRD', which decide who gets PARP inhibitors, rely on genomic scars that reflect the tumour's past, not its present. A test of current DNA-repair function would be better, but needs standardising.

Thousands of tests that could predict who benefits from a treatment are published and never validated. A fund would pay for the boring but essential confirmation studies in independent patient groups.

Thousands of cancer biomarkers are published; almost none reach patients because nobody validates them fairly. Create a public service that tests any candidate blind against stored samples.

Targeted cancer drugs often reach a country years before the test needed to select patients is approved there. Recognising other regulators' test approvals would close the gap.

Between diagnosis and surgery there are usually a few weeks. Giving a new drug in that window and comparing the tumour before and after surgery shows whether it hits its target in real people, quickly and cheaply.

Tumour mutational burden decides who gets immunotherapy in some settings, but every sequencing panel calculates it differently. A shared calibration would make the number mean the same thing everywhere.

Some tumours change fast and escape drugs quickly; others are stable. A single validated score for how evolvable a tumour is would tell doctors how aggressively to combine treatments.

Two people can have identical treatment and only one develops heart failure or a second cancer years later. Collecting blood and genetic data from survivors could reveal who is at risk and who can be reassured.

Some people have only a few spots of spread and can be cured by treating each one. Others have many spots not yet visible. A test to tell them apart would spare futile treatment and find curable patients.

Once a year, send the same blinded blood samples to every company selling a tumour-DNA test and publish how each performed.

Trials often measure a stand-in for survival, such as time until the cancer grows on scans. An independent body would test, for each cancer and treatment type, whether the stand-in actually predicts survival, and publish the answer.

Whether a breast cancer counts as 'HER2-low', and so qualifies for a powerful drug, depends on which lab reads the slide. Standard reference slides with known HER2 levels would make the answer consistent.

Dozens of companies sell blood tests for tumour DNA and they report different results on the same sample. Government-issued reference samples with known amounts of tumour DNA would expose the differences.

Some tumours have broken the machinery that displays their identity to immune cells. Those patients cannot benefit from most immunotherapy and should be routed elsewhere.

Cancer cells travelling in the blood can be counted. If a drug clears them, that is an early sign it may stop spread, and it reads out in weeks rather than years.

A drug label says 'for PD-L1 positive patients' but does not say that other tests give different answers. Labels should list the validated tests and how much they disagree.

If a blood test reliably shows whether cancer will come back after surgery, trials could use it instead of waiting years for relapse. Regulators have a process to bless such a test; oncology should use it.

Lab-grown mini-tumours are already being sold to guide treatment, but the tests are not validated like other medical tests. They should be.

Drugs are described as phase 1, 2 or 3 so everyone knows how proven they are. Biomarkers should carry the same kind of label so a 'promising' marker is not mistaken for a validated one.

A drug aimed at a mutation present in every tumour cell works differently from one aimed at a mutation in only some cells. Test reports should say which is which.

The detailed molecular maps of tumours being built today mostly lack information on what happened to the patient. Require every atlas sample to carry consented outcome data.

Companies sometimes choose the biomarker threshold that makes their trial look best after seeing the data. Requiring the threshold to be fixed and published before the big trial starts prevents this.

When a screening test misses a cancer, we should know. Linking every negative result to the cancer registry and publishing what was missed, by stage, should be a condition of use.

The support cells that build a tumour's scaffolding come in several types: some protect the tumour, others restrain it. Treating all of them the same way explains past failures.

Screening finds cancers that would never have caused harm alongside dangerous ones. Pair every screening test with a test that says which is which, so people with harmless findings can safely watch and wait.

If one lab suddenly starts finding twice as many 'positive' results as others, something has gone wrong with its test. Pooling positivity rates across labs would catch this automatically.

Thousands of papers extract 'radiomic' features from scans to predict outcomes, but the features change with scanner settings. Journals should require standard compliance before any clinical claim is made.

There are several different PD-L1 tests, each tied to a different drug, and they disagree. A single digitally calibrated scale would let any lab's result be translated into any drug's cut-off.

Many patients with advanced lung or bowel cancer start treatment without the gene tests that would show whether a targeted drug would work. Let the pathologist order the full test the moment cancer is confirmed, without waiting for an oncologist.

Insurers pay for many cancer tests that have never been shown to improve outcomes. Paying only inside studies that measure whether the test helps would sort the useful from the useless.

Drug trials must be registered before they start so results cannot be hidden or reshaped. Studies that claim a biomarker predicts outcome should be registered too.

Anyone building a new test for HER2, PD-L1 or tumour DNA should be able to check it against the same public reference set. Today each developer validates on private data nobody can inspect.

Labs already get tested on whether they score biomarkers correctly, but the results are private. Publishing them would let hospitals and patients avoid labs that get it wrong.

Trials often have independent radiologists check whether tumours grew. How often they disagree with the treating doctors is a measure of how trustworthy the result is, and it is rarely published.

Current blood tests miss leftover cancer in many patients. Reading thousands of mutations at once, rather than a few dozen, can detect far smaller amounts.

PSMA scans could measure prostate cancer burden and response far better than PSA, but no one has done the standardisation work to make the measurement trustworthy across scanners.

Many patients are denied a drug because a test says they will not benefit. For the most important tests, that assumption should itself be tested in a trial.

Fluid taken from the lower back contains DNA from brain tumours. Testing it can diagnose, monitor and detect resistance without brain surgery.

How a tissue sample is handled before it reaches the lab changes the results of biomarker tests. That handling time is almost never recorded, so nobody can tell a true negative from a spoiled sample.

Different companies' leftover-cancer blood tests disagree, and there is no shared yardstick. Public reference samples would let anyone check which test actually works.

A test approved to select patients for a drug in the US must go through separate approval in Europe, Japan and elsewhere, delaying the drug. Accepting each other's test approvals would fix the delay.

Trials that fail still collected thousands of blood and tissue samples. Instead of being destroyed, they should be pooled so scientists can learn who might have benefited.

Instead of testing a drug in lab models first and hoping the results carry over, build the same models from trial participants and run both experiments in parallel to see how well the models predict.

Wasting has several causes. Measuring the specific hormone in each patient's blood would put the right patients into the right trial instead of mixing everyone together.

Growing a patient's own tumour-fighting cells only works if those cells are there to start with. A test for them would spare futile treatment.

New microscopes can measure dozens of proteins at once and map where immune cells sit in a tumour. These readouts could predict immunotherapy response, but every lab does it differently.

For each cancer type, agree the set of stains and tests that are always needed, and have the lab run them automatically on diagnosis rather than waiting for someone to ask.

Cancers driven by MYC need to make proteins at an unusually fast rate. Slowing the cell's protein factory hits them harder than it hits normal cells.

Cancer cells enter the blood mostly during rest, so a morning blood test may miss them. Sampling and dosing at the right hour may be a free improvement.

For very rare cancers there is often no genetic clue and no trial. Growing the patient's cells and testing drugs on them directly can suggest what to try.

Many people get a genetic result of uncertain significance, which cannot be acted on. Lab methods now let us test every possible variant in a gene in advance.

Trials that only enrol patients with a positive biomarker can never prove the biomarker matters. Including a smaller biomarker-negative group would show whether the test is really needed.

Whether immune cells are next to cancer cells matters more than how many there are. Turning that spatial picture into a reliable, standardised test would predict response better.

Tumours starved of oxygen produce a chemical that switches immune cells off. A scan can show which tumours are starved, and those are the ones to treat with blockers.

A single protein predicts whether a tumour will respond to DNA-damaging drug payloads. Measuring it could stop patients receiving a second drug of the same kind that will not work.

Pragmatic trials want to use the progression dates recorded in ordinary clinic notes instead of expensive protocol scans. Checking how well those routine records match formal trial measurements would show when that shortcut is safe.

AI is starting to decide which patients get which cancer drug. Every change to the software should be tested against a fixed public set of cases before it is used on patients.

When immunotherapy works, specific immune cell families multiply in the blood within weeks. Tracking that could tell patients early whether to continue.